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1. www.nature.com › articles › s41746/020/00370-8Characteristics and challenges of the clinical pipeline of ...

   www.nature.com › articles › s41746/020/00370-8

   Dec 11, 2020 · In this Comment, we characterize the current pipeline of digital therapeutics and offer a clinical perspective into the advantages, challenges, and barriers to implementation of this treatment ...

   • Author: Nisarg A Patel, Atul J Butte

   • Publish Year: 2020

2. link.springer.com › article › 10Predicting Systolic Blood Pressure in Real-Time Using ...

   link.springer.com › article › 10

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   Sep 4, 2020 · On the other hand, artificial intelligence has played an essential role in advancing hypertension care. In particular, health-based streaming data integrated with AI technologies can detect early signs of hypertension by analyzing data produced from wearable devices.

   • Author: Hager Saleh, Eman M. G. Younis, Radhya Sahal, Radhya Sahal, Abdelmgeid A. Ali

   • Publish Year: 2021

3. www.databricks.com › blog › 2020/10/20Detecting At-risk Patients with Real World Data - Databricks

   www.databricks.com › blog › 2020/10/20

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   Oct 20, 2020 · Learn how to predict which patients are at risk of developing a disease with machine learning on real world data from an EHR.

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4. estuary.dev › streaming-data-pipelinesStreaming Data Pipelines: Must-Have Features & Complexities

   estuary.dev › streaming-data-pipelines

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   Oct 12, 2022 · Overcoming Streaming Data Pipeline Complexities. Ensuring streaming data pipelines can have a lot of complications and risk improper implementation. It can risk the entire data processing workflow and to avoid this from happening, certain complexities need to be kept in mind.

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5. www.ncbi.nlm.nih.gov › pmc › articlesTransforming Health Data to Actionable Information: Recent ...

   www.ncbi.nlm.nih.gov › pmc › articles

   Dec 4, 2022 · The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality.

   • Author: Indra Neil Sarkar
   • Publication: Yearb Med Inform. 2022 Aug; 31(1): 203-214.

   • DOI: 10.1055/s-0042-1742519
   • Published: 2022/08

6. link.springer.com › article › 10A scalable and real-time system for disease prediction using ...

   link.springer.com › article › 10

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   Feb 22, 2023 · The amount of data generated in real-time is becoming very important, which involves a number of problems, the main one being the processing and prediction of streaming data event coming with rapid rate. Solving these problems using traditional technologies require hardware resources and time-consuming for the analysis especially machine learning.

7. People also ask

   Are streaming data pipelines a risk?

   • Ensuring streaming data pipelines can have a lot of complications and risk improper implementation. It can risk the entire data processing workflow and to avoid this from happening, certain complexities need to be kept in mind. Ensure that your business avoids the following pitfalls when implementing real-time data streaming:

   Streaming Data Pipelines: Must-Have Features & Complexities

   estuary.dev/streaming-data-pipelines/

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   Are streaming data pipelines right for your business?

   • Streaming data pipelines emerged as the obvious choice for most businesses and they’ve been at the cornerstone of real-time data delivery ever since. But some businesses and IT engineers aren’t familiar with data pipelines altogether and struggle with their implementation or run into various roadblocks along the way.

   Streaming Data Pipelines: Must-Have Features & Complexities

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   What is a streaming data pipeline?

   • Streaming data pipelines are built on technology systems called event brokers, otherwise known as event buses. They are event driven and move data in real time (or as close to real time as possible). What does this mean? The event broker detects changes of state in the source data — when a piece of data is added, updated, or deleted.

   Streaming Data Pipelines: Must-Have Features & Complexities

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   What pitfalls should your business avoid when implementing real-time data streaming?

   • Ensure that your business avoids the following pitfalls when implementing real-time data streaming: Stream processing stacks can often be tightly bound despite most pipeline concepts urging for loose coupling practices. Stream processing needs to be open and flexible with IT teams carefully considering how to approach its design and implementation.

   Streaming Data Pipelines: Must-Have Features & Complexities

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8. www.informatica.com › blogs › apply-streamingApply Streaming Analytics Technology to Improve Medical Care

   www.informatica.com › blogs › apply-streaming

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   Apr 25, 2022 · Streaming analytics can help to analyze data from wearable devices and use machine-learning models to assess the risk of patients’ glucose levels falling outside the safe threshold.